The experiment uses two types of corpora: one for a translation model and the other for a language model.
Thus, training data of the Asian Scientific Paper Excerpt Corpus (ASPEC) [1]} are divided into two parts: a parallel corpus and a monolingual corpus.
The parallel corpus, for translation, is composed of one million sentences with a high confidence of sentence alignment from the training data.
The monolingual corpus, for language models, is composed of two million sentences from the target side of the training data that are not used in the parallel corpus.
Japanese sentences were tokenized by the morphological analyzer MeCab https://github.com/taku910/mecab (IPADic), and English sentences were preprocessed by Moses http://www.statmt.org/moses/ (tokenizer, truecaser).
We used development and evaluation set on the official partitioning of ASPEC as summarized in Table  REFWe exclude sentences whose number of tokens with more than 60 tokens in training..
Vocabulary is determined using only the parallel corpus.
For example, words existing only in the monolingual corpus are treated as unknown words at testing, even if they frequently appear in the monolingual corpus to train the language model.
Additionally, experiments have been conducted with and without Byte Pair Encoding (BPE) [2]}.
BPE was performed on the source side and target side separately.
